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feat: ✨ added the extraction proces into the main multithreaded loop

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Also added a warning when the app finds existing CSV files in the combined folder
This commit is contained in:
Jake Pullen
2025-12-12 18:35:26 +00:00
parent ad6b31e644
commit 2c4c4a3f4e
7 changed files with 213 additions and 81 deletions
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main.py
+142 -46
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@@ -4,6 +4,7 @@ import os
import csv
import concurrent.futures
from pathlib import Path
import shutil
from config import Config
from modules import BatchNimrod, GenerateTimeseries, Extract
@@ -13,14 +14,40 @@ logging.basicConfig(
)
def process_pipeline(dat_file):
# 1. Process DAT to ASC
asc_file = batch._process_single_file(dat_file)
if not asc_file:
def process_pipeline(gz_file_path):
# 1. Extract GZ to DAT
gz_path = Path(gz_file_path)
# The dat file name is derived from the gz file name (removing .gz or .dat.gz)
# gz files are named like 'NAME.dat.gz' often.
dat_filename = gz_path.name.replace(".gz", "")
dat_path = Path(Config.DAT_TOP_FOLDER, dat_filename)
# Extract
try:
extraction.process_single_gz(gz_path, dat_path)
except Exception as e:
logging.error(f"Failed to extract {gz_path}: {e}")
return None
# 2. Extract data from ASC
if not dat_path.exists():
logging.error(f"DAT file not found after extraction: {dat_path}")
return None
# 2. Process DAT to ASC
# BatchNimrod._process_single_file expects just the filename, not full path
asc_file = batch._process_single_file(dat_filename)
if not asc_file:
# Cleanup failed DAT file if needed (BatchNimrod might have done it or not)
if Config.delete_dat_after_processing and dat_path.exists():
try:
os.remove(dat_path)
except OSError:
pass
return None
# 3. Extract data from ASC
file_results = timeseries.process_asc_file(asc_file, locations)
return file_results
@@ -57,63 +84,132 @@ if __name__ == "__main__":
logging.info(f"Count of 1km Grids: {len(locations)}")
logging.info(f"Count of Zones: {len(zones)}")
# Check for existing combined files
existing_combined = os.listdir(Config.COMBINED_FOLDER)
if existing_combined:
logging.warning("!" * 80)
logging.warning(
f"Found {len(existing_combined)} files in {Config.COMBINED_FOLDER}"
)
logging.warning(
"You may want to remove these before continuing to avoid duplicates or messy data."
)
logging.warning("!" * 80)
response = input("Continue? (Y/N): ").strip().lower()
if response != "y":
logging.info("Aborting...")
exit(0)
extraction = Extract(Config)
batch = BatchNimrod(Config)
timeseries = GenerateTimeseries(Config, locations)
start = time.time()
logging.info(
"Starting interleaved processing of DAT files and Timeseries generation"
"Starting interleaved processing of GZ files -> DAT -> ASC -> Timeseries"
)
# Initialize results structure
results = {loc[0]: {"dates": [], "values": []} for loc in locations}
# Get list of all tar files
all_tar_files = [f for f in os.listdir(Config.TAR_TOP_FOLDER) if f.endswith(".tar")]
all_tar_files.sort()
total_tars = len(all_tar_files)
files_per_tar = 288
estimated_total_files = total_tars * files_per_tar
logging.info(f"Found {total_tars} tar files to process")
logging.info("Extracting tar and gz files")
extraction.run_extraction()
# Process in batches
for i in range(0, total_tars, Config.BATCH_SIZE):
batch_files = all_tar_files[i : i + Config.BATCH_SIZE]
logging.info(
f"Processing batch {i // Config.BATCH_SIZE + 1}: {len(batch_files)} tar files"
)
# Get list of DAT files
dat_files = [
f for f in os.listdir(Path(Config.DAT_TOP_FOLDER)) if not f.startswith(".")
]
total_files = len(dat_files)
# Initialize results structure for this batch
results = {loc[0]: {"dates": [], "values": []} for loc in locations}
logging.info(f"Processing {total_files} files concurrently...")
# 1. Extract batch (TAR -> GZ)
logging.info("Extracting tar files for batch")
extraction.extract_tar_batch(batch_files)
# Note: We do NOT run extract_gz_batch anymore. We will find GZ files and process them.
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_file = {
executor.submit(process_pipeline, dat_file): dat_file
for dat_file in dat_files
}
# Get list of GZ files (recursively or flat?)
# extract_tar_batch puts them in GZ_TOP_FOLDER/tar_name_without_ext
# So we need to look there.
# Ideally we know where we put them.
completed_count = 0
try:
for future in concurrent.futures.as_completed(future_to_file):
file_results = future.result()
if file_results:
for res in file_results:
zone_id = res["zone_id"]
results[zone_id]["dates"].append(res["date"])
results[zone_id]["values"].append(res["value"])
gz_files_to_process = []
for tar_file in batch_files:
extract_folder = Path(Config.GZ_TOP_FOLDER, tar_file.replace(".tar", ""))
if extract_folder.exists():
for root, _, files in os.walk(extract_folder):
for file in files:
if file.endswith(".gz"):
gz_files_to_process.append(Path(root, file))
completed_count += 1
if completed_count % 100 == 0:
elapsed_time = time.time() - start
files_per_minute = (completed_count / elapsed_time) * 60
remaining_files = total_files - completed_count
eta_minutes = remaining_files / (files_per_minute / 60) / 60
logging.info(f"""Processed {completed_count} out of {total_files} files.
Speed: {files_per_minute:.2f} files/min. ETA: {eta_minutes:.2f} minutes""")
except KeyboardInterrupt:
logging.warning("KeyboardInterrupt received. Cancelling pending tasks...")
executor.shutdown(wait=False, cancel_futures=True)
raise
total_files = len(gz_files_to_process)
logging.info(f"Found {total_files} GZ files to process concurrently...")
elapsed_time = time.time() - start
logging.info(f"Interleaved processing completed in {elapsed_time:.2f} seconds")
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_file = {
executor.submit(process_pipeline, gz_file): gz_file
for gz_file in gz_files_to_process
}
logging.info("Writing CSV files...")
timeseries.write_results_to_csv(results, locations)
completed_count = 0
try:
for future in concurrent.futures.as_completed(future_to_file):
file_results = future.result()
if file_results:
for res in file_results:
zone_id = res["zone_id"]
results[zone_id]["dates"].append(res["date"])
results[zone_id]["values"].append(res["value"])
completed_count += 1
if completed_count % 100 == 0:
elapsed_time = time.time() - start
rate_per_second = completed_count / elapsed_time
files_processed_previous = i * files_per_tar
files_processed_so_far = (
files_processed_previous + completed_count
)
remaining_files = estimated_total_files - files_processed_so_far
if rate_per_second > 0:
eta_seconds = remaining_files / rate_per_second
if eta_seconds < 60:
eta_str = f"{int(eta_seconds)}s"
elif eta_seconds < 3600:
eta_str = f"{int(eta_seconds // 60)}m {int(eta_seconds % 60)}s"
else:
eta_str = f"{int(eta_seconds // 3600)}h {int((eta_seconds % 3600) // 60)}m"
else:
eta_str = "Unknown"
logging.info(f"""Progress: {files_processed_so_far}/{estimated_total_files} files ({files_processed_so_far / estimated_total_files * 100:.1f}%)
Speed: {rate_per_second * 60:.2f} files/min. ETA: {eta_str}""")
except KeyboardInterrupt:
logging.warning(
"KeyboardInterrupt received. Cancelling pending tasks..."
)
executor.shutdown(wait=False, cancel_futures=True)
raise
logging.info("Appending batch results to CSV files...")
timeseries.append_results_to_csv(results, locations)
# Cleanup GZ folders for this batch
# We loop through batch_files again to delete the folders we created
for tar_file in batch_files:
extract_folder = Path(Config.GZ_TOP_FOLDER, tar_file.replace(".tar", ""))
if extract_folder.exists():
try:
shutil.rmtree(extract_folder)
except OSError as e:
logging.warning(f"Failed to remove GZ folder {extract_folder}: {e}")
end = time.time()
elapsed_time = end - start